
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowercase    :   List[Any]						=			logging.getLogger(__name__)




@dataclass
class 					lowerCamelCase__				:




	'''simple docstring'''




	_A								=	42
	_A								=	42
	_A								=	42




@dataclass
class 					lowerCamelCase__				:




	'''simple docstring'''




	_A								=	42
	_A								=	42
	_A								=	None
	_A								=	None




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'train'
	_A								=	'dev'
	_A								=	'test'




class 					lowerCamelCase__				:




	'''simple docstring'''




	@staticmethod
	def   _lowerCamelCase							(   a						:Optional[int]	,  a						:Union[Split, str]		)						->					List[InputExample]:
								raise NotImplementedError







	@staticmethod
	def   _lowerCamelCase							(   a						:str		)						->					List[str]:
								raise NotImplementedError







	@staticmethod
	def   _lowerCamelCase							(   a						:List[InputExample]	,  a						:List[str]	,  a						:int	,  a						:PreTrainedTokenizer	,  a						:Optional[int]=False	,  a						:List[Any]="[CLS]"	,  a						:Optional[int]=1	,  a						:Union[str, Any]="[SEP]"	,  a						:Tuple=False	,  a						:Dict=False	,  a						:Optional[int]=0	,  a						:Union[str, Any]=0	,  a						:Tuple=-1_0_0	,  a						:str=0	,  a						:Any=True	,  )						->					List[InputFeatures]:
								__UpperCamelCase      :  Union[str, Any]       =   {label: i for i, label in enumerate(a		)}

								__UpperCamelCase      :  Tuple       =   []
								for ex_index, example in enumerate(a		):
															if ex_index % 1_0_0_0_0 == 0:
																						logger.info("Writing example %d of %d"	,  a	,  len(a		)		)

															__UpperCamelCase      :  List[str]       =   []
															__UpperCamelCase      :  Dict       =   []
															for word, label in zip(example.words	,  example.labels		):
																						__UpperCamelCase      :  int       =   tokenizer.tokenize(a		)

																						# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																						if len(a		) > 0:
																													tokens.extend(a		)
																													# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																													label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(a		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
															__UpperCamelCase      :  Union[str, Any]       =   tokenizer.num_special_tokens_to_add()
															if len(a		) > max_seq_length - special_tokens_count:
																						__UpperCamelCase      :  Optional[Any]       =   tokens[: (max_seq_length - special_tokens_count)]
																						__UpperCamelCase      :  str       =   label_ids[: (max_seq_length - special_tokens_count)]

															# The convention in BERT is:
															# (a) For sequence pairs:
															#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
															#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
															# (b) For single sequences:
															#  tokens:   [CLS] the dog is hairy . [SEP]
															#  type_ids:   0   0   0   0  0     0   0
															#
															# Where "type_ids" are used to indicate whether this is the first
															# sequence or the second sequence. The embedding vectors for `type=0` and
															# `type=1` were learned during pre-training and are added to the wordpiece
															# embedding vector (and position vector). This is not *strictly* necessary
															# since the [SEP] token unambiguously separates the sequences, but it makes
															# it easier for the model to learn the concept of sequences.
															#
															# For classification tasks, the first vector (corresponding to [CLS]) is
															# used as the "sentence vector". Note that this only makes sense because
															# the entire model is fine-tuned.
															tokens += [sep_token]
															label_ids += [pad_token_label_id]
															if sep_token_extra:
																						# roberta uses an extra separator b/w pairs of sentences
																						tokens += [sep_token]
																						label_ids += [pad_token_label_id]
															__UpperCamelCase      :  Tuple       =   [sequence_a_segment_id] * len(a		)

															if cls_token_at_end:
																						tokens += [cls_token]
																						label_ids += [pad_token_label_id]
																						segment_ids += [cls_token_segment_id]
															else:
																						__UpperCamelCase      :  int       =   [cls_token] + tokens
																						__UpperCamelCase      :  Tuple       =   [pad_token_label_id] + label_ids
																						__UpperCamelCase      :  str       =   [cls_token_segment_id] + segment_ids

															__UpperCamelCase      :  int       =   tokenizer.convert_tokens_to_ids(a		)

															# The mask has 1 for real tokens and 0 for padding tokens. Only real
															# tokens are attended to.
															__UpperCamelCase      :  Optional[Any]       =   [1 if mask_padding_with_zero else 0] * len(a		)

															# Zero-pad up to the sequence length.
															__UpperCamelCase      :  Optional[int]       =   max_seq_length - len(a		)
															if pad_on_left:
																						__UpperCamelCase      :  Optional[int]       =   ([pad_token] * padding_length) + input_ids
																						__UpperCamelCase      :  str       =   ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																						__UpperCamelCase      :  Tuple       =   ([pad_token_segment_id] * padding_length) + segment_ids
																						__UpperCamelCase      :  Any       =   ([pad_token_label_id] * padding_length) + label_ids
															else:
																						input_ids += [pad_token] * padding_length
																						input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																						segment_ids += [pad_token_segment_id] * padding_length
																						label_ids += [pad_token_label_id] * padding_length

															assert len(a		) == max_seq_length
															assert len(a		) == max_seq_length
															assert len(a		) == max_seq_length
															assert len(a		) == max_seq_length

															if ex_index < 5:
																						logger.info("*** Example ***"		)
																						logger.info("guid: %s"	,  example.guid		)
																						logger.info("tokens: %s"	,  " ".join([str(a		) for x in tokens]		)		)
																						logger.info("input_ids: %s"	,  " ".join([str(a		) for x in input_ids]		)		)
																						logger.info("input_mask: %s"	,  " ".join([str(a		) for x in input_mask]		)		)
																						logger.info("segment_ids: %s"	,  " ".join([str(a		) for x in segment_ids]		)		)
																						logger.info("label_ids: %s"	,  " ".join([str(a		) for x in label_ids]		)		)

															if "token_type_ids" not in tokenizer.model_input_names:
																						__UpperCamelCase      :  Any       =   None

															features.append(
															    InputFeatures(
															        input_ids=a	,  attention_mask=a	,  token_type_ids=a	,  label_ids=a		)		)
								return features


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset




		class 					lowerCamelCase__				(							__lowercase):




			'''simple docstring'''




			_A								=	42
			_A								=	nn.CrossEntropyLoss().ignore_index







			def __init__(   self						:Any	,  a						:TokenClassificationTask	,  a						:str	,  a						:PreTrainedTokenizer	,  a						:List[str]	,  a						:str	,  a						:Optional[int] = None	,  a						:List[Any]=False	,  a						:Split = Split.train	,  )						->					Optional[int]:
										# Load data features from cache or dataset file
										__UpperCamelCase      :  Optional[Any]       =   os.path.join(
										    a	,  "cached_{}_{}_{}".format(mode.value	,  tokenizer.__class__.__name__	,  str(a		)		)	,  )

										# Make sure only the first process in distributed training processes the dataset,
										# and the others will use the cache.
										__UpperCamelCase      :  Optional[Any]       =   cached_features_file + ".lock"
										with FileLock(a		):
																	if os.path.exists(a		) and not overwrite_cache:
																								logger.info(f'Loading features from cached file {cached_features_file}'		)
																								__UpperCamelCase      :  Any       =   torch.load(a		)
																	else:
																								logger.info(f'Creating features from dataset file at {data_dir}'		)
																								__UpperCamelCase      :  Optional[Any]       =   token_classification_task.read_examples_from_file(a	,  a		)
																								# TODO clean up all this to leverage built-in features of tokenizers
																								__UpperCamelCase      :  Optional[Any]       =   token_classification_task.convert_examples_to_features(
																								    a	,  a	,  a	,  a	,  cls_token_at_end=bool(model_type in ["xlnet"]		)	,  cls_token=tokenizer.cls_token	,  cls_token_segment_id=2 if model_type in ["xlnet"] else 0	,  sep_token=tokenizer.sep_token	,  sep_token_extra=a	,  pad_on_left=bool(tokenizer.padding_side == "left"		)	,  pad_token=tokenizer.pad_token_id	,  pad_token_segment_id=tokenizer.pad_token_type_id	,  pad_token_label_id=self.pad_token_label_id	,  )
																								logger.info(f'Saving features into cached file {cached_features_file}'		)
																								torch.save(self.features	,  a		)







			def __len__(   self						:Optional[Any]		)						->					Dict:
										return len(self.features		)







			def __getitem__(   self						:Dict	,  a						:List[Any]		)						->					InputFeatures:
										return self.features[i]


if is_tf_available():
		import tensorflow as tf




		class 					lowerCamelCase__				:




			'''simple docstring'''




			_A								=	42
			_A								=	-1_0_0







			def __init__(   self						:Optional[int]	,  a						:TokenClassificationTask	,  a						:str	,  a						:PreTrainedTokenizer	,  a						:List[str]	,  a						:str	,  a						:Optional[int] = None	,  a						:int=False	,  a						:Split = Split.train	,  )						->					Optional[Any]:
										__UpperCamelCase      :  Tuple       =   token_classification_task.read_examples_from_file(a	,  a		)
										# TODO clean up all this to leverage built-in features of tokenizers
										__UpperCamelCase      :  int       =   token_classification_task.convert_examples_to_features(
										    a	,  a	,  a	,  a	,  cls_token_at_end=bool(model_type in ["xlnet"]		)	,  cls_token=tokenizer.cls_token	,  cls_token_segment_id=2 if model_type in ["xlnet"] else 0	,  sep_token=tokenizer.sep_token	,  sep_token_extra=a	,  pad_on_left=bool(tokenizer.padding_side == "left"		)	,  pad_token=tokenizer.pad_token_id	,  pad_token_segment_id=tokenizer.pad_token_type_id	,  pad_token_label_id=self.pad_token_label_id	,  )

										def gen():
																	for ex in self.features:
																								if ex.token_type_ids is None:
																															yield (
																															    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																															    ex.label_ids,
																															)
																								else:
																															yield (
																															    {
																															        "input_ids": ex.input_ids,
																															        "attention_mask": ex.attention_mask,
																															        "token_type_ids": ex.token_type_ids,
																															    },
																															    ex.label_ids,
																															)

										if "token_type_ids" not in tokenizer.model_input_names:
																	__UpperCamelCase      :  Tuple       =   tf.data.Dataset.from_generator(
																	    a	,  ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)	,  (
																	        {"input_ids": tf.TensorShape([None]		), "attention_mask": tf.TensorShape([None]		)},
																	        tf.TensorShape([None]		),
																	    )	,  )
										else:
																	__UpperCamelCase      :  int       =   tf.data.Dataset.from_generator(
																	    a	,  ({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)	,  (
																	        {
																	            "input_ids": tf.TensorShape([None]		),
																	            "attention_mask": tf.TensorShape([None]		),
																	            "token_type_ids": tf.TensorShape([None]		),
																	        },
																	        tf.TensorShape([None]		),
																	    )	,  )







			def   _lowerCamelCase							(   self						:str		)						->					int:
										__UpperCamelCase      :  Dict       =   self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

										return self.dataset







			def __len__(   self						:Optional[Any]		)						->					Optional[Any]:
										return len(self.features		)







			def __getitem__(   self						:Dict	,  a						:Any		)						->					InputFeatures:
										return self.features[i]

from __future__ import annotations




class 					lowerCamelCase__				:




	'''simple docstring'''




	def __init__(   self						:Dict	,  a						:str	,  a						:str		)						->					Union[str, Any]:
								__UpperCamelCase  ,		__UpperCamelCase      :  Optional[int]       =   text, pattern
								__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   len(a		), len(a		)







	def   _lowerCamelCase							(   self						:Any	,  a						:str		)						->					int:

								for i in range(self.patLen - 1	,  -1	,  -1		):
															if char == self.pattern[i]:
																						return i
								return -1







	def   _lowerCamelCase							(   self						:str	,  a						:int		)						->					int:

								for i in range(self.patLen - 1	,  -1	,  -1		):
															if self.pattern[i] != self.text[current_pos + i]:
																						return current_pos + i
								return -1







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					list[int]:
								# searches pattern in text and returns index positions
								__UpperCamelCase      :  Any       =   []
								for i in range(self.textLen - self.patLen + 1		):
															__UpperCamelCase      :  List[Any]       =   self.mismatch_in_text(a		)
															if mismatch_index == -1:
																						positions.append(a		)
															else:
																						__UpperCamelCase      :  Any       =   self.match_in_pattern(self.text[mismatch_index]		)
																						__UpperCamelCase      :  Dict       =   (
																						    mismatch_index - match_index
																						)  # shifting index lgtm [py/multiple-definition]
								return positions
lowercase    :   Any						=			'ABAABA'
lowercase    :   str						=			'AB'
lowercase    :   str						=			BoyerMooreSearch(text, pattern)
lowercase    :   Union[str, Any]						=			bms.bad_character_heuristic()

if len(positions) == 0:
		print('No match found')
else:
		print('Pattern found in following positions: ')
		print(positions)